An Improved Dynamic Window Approach Based on Reinforcement Learning for the Trajectory Planning of Automated Guided Vehicles
Da Jiang,
Ling Du,
Shuhui Li
et al.
Abstract:The traditional dynamic window approach (DWA) adopts the constant intervals for the sampling window, which limits the trajectory exploration possibility. This paper employs the twin delayed deep deterministic policy gradient (TD3) approach to generate a reinforcement-learning-based auxiliary candidate trajectory with variable sampling mechanism in the prediction domain for the automated guided vehicle (AGV). Subsequently, this auxiliary trajectory would compete with the traditional DWA sampling trajectories in… Show more
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